Abstract
A Wireless Mesh Network (WMN) is an effective solution to provide Internet connectivity to large areas and its efficiency may increase if multiple radio channels are used in the mesh backbone. This paper proposes a protocol for centralized channel assignment in single-radio WMNs. This protocol has the capability to discover all the links available between Mesh Access Points (MAPs), independently of the channel they operate. With this information, a network manager can assign the right channel to each MAP in order to, for instance, maximize the network throughput. The proposed protocol extends WiFIX [1] which is a low overhead solution for implementing IEEE 802.11-based WMNs. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

Abstract
Cardiotocography is widely used, all over the world, for fetal heart rate and uterine contractions monitoring before (antepartum) and during (intrapartum) labor, regarding the detection of fetuses in danger of death or permanent damage. However, analysis of cardiotocogram tracings remains a large and unsolved issue. State-of-the-art monitoring systems provide quantitative parameters that are difficult to assess by the human eye. These systems also trigger alerts for changes in the behavior of the signals. However, they usually take up to 10 min to detect these different behaviors. Previous work using machine learning for concept drift detection has successfully achieved faster results in the detection of such events. Our aim is to extend the monitoring system with memory-less fading statistics, which have been successfully applied in drift detection and statistical tests, to improve detection of alarming events.

Abstract
Jokes classification is an intrinsically subjective and complex task, mainly due to the difficulties related to cope with contextual constraints on classifying each joke. Nowadays people have less time to devote to search and enjoy humour and, as a consequence, people are usually interested on having a set of interesting filtered jokes that could be worth reading, that is with a high probability of make them laugh. In this paper we propose a crowdsourcing based collective intelligent mechanism to classify humour and to recommend the most interesting jokes for further reading. Crowdsourcing is becoming a model for problem solving, as it revolves around using groups of people to handle tasks traditionally associated with experts or machines. We put forward an active learning Support Vector Machine (SVM) approach that uses crowdsourcing to improve classification of user custom preferences. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results.

Abstract
The application of organic wastes to agricultural soils is not risk-free and can affect soil invertebrates. Ecotoxicological tests based on the behavioral avoidance of earthworms and springtails were performed to evaluate effects of different fertilization strategies on soil quality and habitat function for soil organisms. These tests were performed in soils treated with: i) slurry and chemical fertilizers, according to the conventional fertilization management of the region, ii) conventional fertilization + sludge and iii) unfertilized reference soil. Both fertilization strategies contributed to soil acidity mitigation and caused no increase in soil heavy metal content. Avoidance test results showed no negative effects of these strategies on soil organisms, compared with the reference soil. However, results of the two fertilization managements differed: Springtails did not avoid soils fertilized with dairy sludge in any of the tested combinations. Earthworms avoided soils treated with sludge as of May 2004 (DS1), when compared with conventional fertilization. Possibly, the behavioral avoidance of earthworms is more sensitive to soil properties (other than texture, organic matter and heavy metal content) than springtails

Abstract
Clinical record integration and visualization is one of the most important abilities of modern health information systems (HIS). Its use on clinical encounters plays a relevant role in the efficacy and efficiency of healthcare. However, integrated HIS of central hospitals may gather millions of clinical reports (e.g. radiology, lab results, etc.). Hence, the clinical record must manage a stream of reports being produced in the entire hospital. Moreover, not all documents from a patient are relevant for a given encounter, and therefore not visualized during that encounter. Thus, the HIS must also manage a stream of events of visualization of reports, which runs in parallel to the stream of documents production. The aim of our project is to provide the physician with a recommendation of clinical reports to consider when they log in the computer. Our approach is to model relevance as the probability that a given document will be accessed in the current time frame. For that, we design a data stream management system to process the two streams, and Bayesian networks to learn those probabilities based on document, patient, department and user information. One of the biggest challenges to the learning problem, so far, is that no negative examples are produced by the stream (i.e. there are no record of documents not being visualized) leading to a one-class classification problem. The aim of this paper is to clearly present the setting and rationale for the approach. Current work is focused on both the stream processing mechanism and the Bayesian probability estimation.

Abstract
Humour classification is one of the most interesting and difficult tasks in text classification. Humour is subjective by nature, yet humans are able to promptly define their preferences. Nowadays people often search for humour as a relaxing proxy to overcome stressful and demanding situations, having little or no time to search contents for such activities. Hence, we propose to aid the definition of personal models that allow the user to access humour with more confidence on the precision of his preferences. In this paper we focus on a Support Vector Machine (SVM) active learning strategy that uses specific most informative examples to improve baseline performance. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results on the proposed framework. © 2011 Springer-Verlag.